A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

Easy Custom Losses for Tree Boosters using Pytorch

Classifying Audio to Emotion

This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

Multi-Class Obesity Risk Prediction Project | Prediction of obesity risk in individuals using various factors, which is related to cardiovascular disease.

Mohs Hardness Prediction Project | Ensemble Models with Neural Networks, LGBM, CAT, XGB using a Voting Mechanism. 🚀💎

No-Caffeine-No-Gain's Deep Knowledge Tracing (DKT)

Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.

A Machine Learning project for Machine Learning Internship offered by InternshipStudio.

Crypto & Stock* price prediction with regression models.

Проекты из Яндекс Практикума "Специалист по Data Science"

Machine learning solutions for the American Express credit default prediction Kaggle competition

BigData: The goal is to be able to create a model capable of predicting the taxi fare in New York.

Practicum Workshop

The complete collection of BPR's code, data, and visualizations for our 2024 election model

농산물 데이터 분석을 통한 가격 예측 AI model별 비교

Attack Detection, Parameter Optimization and Performance Analysis in Enterprise Networks (ML Networks) for Intrusion Detection System IDS.

Using machine learning models to predict the probability of a windows system getting infected by various families of malware, based on different properties of that system.

integrated neural network library